I00025 (I00025)
Computational Intelligence*
< 2006/2007 > 12-02-2007 t/m 01-07-2007 () L
Informatica - Master variant C (2003) Thematische specialisatie Kunstmatige Intelligentie (6 ec) Keuze informatica (6 ec)
Informatica - Master variant E (2003) Keuze informatica (6 ec)
Informatica - Master variant MT (2005) Thematische specialisatie Kunstmatige Intelligentie (6 ec) Kunstmatige Intelligentie (6 ec) Keuze informatica (6 ec) (6 ec) (6 ec)
Informatica - Master variant O (2003) Thematische specialisatie Kunstmatige Intelligentie (6 ec)
Informatica - Master variant O (2005) Thematische specialisatie Kunstmatige Intelligentie (6 ec) Keuze informatica (6 ec)
Informatica - Master na HBO Artificial Intelligence variant MT (2004) Thematische specialisatie Kunstmatige Intelligentie (6 ec)
Informatica - Master na HBO Artificial Intelligence variant O (2004) Thematische specialisatie Kunstmatige Intelligentie (6 ec)
Informatica - Master na HBO Computer Security variant MT (2003) Keuze informatica (6 ec)
Informatica - Master na HBO Embedded Systems variant MT (2003) Keuze informatica (6 ec)
Informatica - Master na HBO Information Systems variant MT (2003) Keuze informatica (6 ec)
Informatica - Master na HBO Software Construction variant MT (2003) Keuze informatica (6 ec)
omvang
6 ec (168 uur) : 50 uur plenair college, 0 uur groepsgewijs college, 10 uur computerpracticum, 0 uur 'droog' practicum, 10 uur gesprekken met de docent, 20 uur onderling overleg met medestudenten (werkgroepen, projectwerk e.d.), 78 uur zelfstudie
investering
6 ec * 28 u/ec + #std * (1 + 6ec * 0.15 u/student/ec)
semester wordt lente

examinator
afdeling
tijdbesteding

prof. dr. Peter Lucas
sws
235u.

speciale web-site
http://www.cs.kun.nl/~peterl/teaching/CI/

 

Handling uncertain knowledge has been one of the central problems of AI research during the past 30 years. In the 1970s and 1980s uncertainty was handled by means of formalisms that were linked to rule-based representation and reasoning methods. Since the 1990s probabilistic graphical models, in particular Bayesian networks, are seen as the primary formalisms to deal with uncertain knowledge. Both early and new methods for represensenting uncertainty are studied in the course, where in particular various aspects of Bayesian networks are covered.

Leerdoelen

At the end of this course, the student should be able to:

  • understand the principles of reasoning under uncertainty
  • understand different numerical models for the representation of uncertainty, such as the CF model, the subjective Bayesian method, Bayesian belief networks, and possibly Dempster-Shafer theory
  • have insight into model-based approaches to AI
  • have insight into the pros and cons of learning models versus using expert knowledge
  • have some experience in experimenting with computational intelligence systems to solve problems involving probability theory

Onderwerpen

  • Introduction to Computational Intelligence
  • Early models of uncertainty
  • Probability theory
  • Bayesian networks: principles
  • Markov independence
  • Reasoning with Bayesian networks
  • Building Bayesian networks
  • Learning Bayesian networks

Werkvormen

  • lectures
  • tutorials
  • practical
  • seminar

Tentaminering

Written exam in addition to practical work.

Combinatiemogelijkheden

The course is part of the AI theme.

Literatuur

  • P.J.F. Lucas and L.C. van der Gaag, Principles of Expert Systems, Addison-Wesley, Wokingham, 1991, Chapter 5.
  • K.B. Korb and A.E. Nicholson, Bayesian Artificial Intelligence, Chapman & Hall, Boca Raton, 2004.
  • R.G. Cowell, A.P. Dawid, S.L. Lauritzen and D.J.Spiegelhalter, Probabilistic Networks and Expert Systems, Springer, New York, 1999.
  • F.V. Jensen, Bayesian Networks and Decision Graphs, Springer, New York, 2001.


Evaluatie: studentenquêtes ; geen docentevaluatie bekend Rendement: 18 begonnen, 16 echt meegedaan, 15 geslaagd met 1e kans, 16 geslaagd totaal
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